Tecnologias web para construção de cadastros de área em pesquisas agropecuárias

CASTLab

Test

Outline

Test

Topics

Area Frames

Test

Crowdsourcing

Test

Crowdsourcing

Definition

Toy model (Uruguay)

set.seed(123)
library(rgdal)
library(raster)
library(rgeos)
library(tidyverse)
uruguay <- getData("GADM", country = "URY", level = 0)
uruguay_projected <- spTransform(uruguay, CRS("+init=epsg:3857"))
forest_projected <- uruguay_projected %>%
  spsample(200, type = "random") %>%
  # build random poisson buffers around them
  gBuffer(width = 4000 * rpois(length(.), 3), byid = TRUE) %>%
  gEnvelope(byid = TRUE) %>% # take their envelope
  gUnaryUnion()

forest_projected <- gIntersection(uruguay_projected, forest_projected)
forest <- spTransform(forest_projected, CRSobj = crs(uruguay))

Toy model: landscape (Uruguay)

library(leaflet)
map <- leaflet() %>% addTiles() %>% addPolygons(data = uruguay, weight = 1) %>% 
  addPolygons(data = forest, weight = 1, fillColor = "green", color = "darkgreen")

Toy model: solution (Uruguay)

buffers_projected <- uruguay_projected %>%
  spsample(10, type = "random") %>%
  gBuffer(width = 30000, byid = TRUE)

buffers <- spTransform(buffers_projected, CRSobj = crs(uruguay))

# Intersect the buffers with the forest and plot the result
intersect_projected <- gIntersection(forest_projected,
  buffers_projected,
  byid = TRUE
)
intersect <- spTransform(intersect_projected, CRSobj = crs(uruguay))

# Compute the proportion of forested areas within each buffer
proportion <- gArea(intersect_projected, byid = TRUE) /
  gArea(buffers_projected, byid = TRUE)

intersect.spdf <- SpatialPolygonsDataFrame(
  intersect,
  data.frame(Proportion = proportion)
)

Toy model: solution (Uruguay)

map %>%
  addPolygons(data = buffers, weight = 2, fillOpacity = 0) %>%
  addPolygons(
    data = intersect.spdf, fillColor = "red", fillOpacity = .9, weight = 2,
    label = ~ scales::percent(Proportion),
    labelOptions = labelOptions(opacity = .75, direction = "auto")
  )

Toy model (Colombia)

set.seed(123)
library(rgdal)
library(raster)
library(rgeos)
library(tidyverse)
colombia <- getData("GADM", country = "COL", level = 0)
colombia_projected <- spTransform(colombia, CRS("+init=epsg:3857"))
forest_projected <- colombia_projected %>%
  spsample(400, type = "random") %>%
  # build random poisson buffers around them
  gBuffer(width = 4000 * rpois(length(.), 3), byid = TRUE) %>%
  gEnvelope(byid = TRUE) %>% # take their envelope
  gUnaryUnion()

forest_projected <- gIntersection(colombia_projected, forest_projected)
forest <- spTransform(forest_projected, CRSobj = crs(uruguay))

Toy model: landscape (Colombia)

library(leaflet)
map <- leaflet() %>% addTiles() %>% addPolygons(data = colombia, weight = 1) %>% 
  addPolygons(data = forest, weight = 1, fillColor = "green", color = "darkgreen")

Toy model: solution (Colombia)

buffers_projected <- colombia_projected %>%
  spsample(20, type = "random") %>%
  gBuffer(width = 30000, byid = TRUE)
buffers <- spTransform(buffers_projected, CRSobj = crs(colombia))
# Intersect the buffers with the forest and plot the result
intersect_projected <- gIntersection(forest_projected,
  buffers_projected,
  byid = TRUE
)
intersect <- spTransform(intersect_projected, CRSobj = crs(colombia))
# Compute the proportion of forested areas within each buffer
intersections <- gArea(intersect_projected, byid = TRUE) %>%
  tibble(ID = names(.), Area_intersection = .) %>%
  separate(ID, into = c("Grid", "ID"))
buffers_areas <- gArea(buffers_projected, byid = TRUE) %>% tibble(ID = names(.), Area_buffers = .)
proportion <- left_join(intersections, buffers_areas, by = "ID") %>%
  mutate(proportion = Area_intersection / Area_buffers, label = paste0(round(100 * proportion, 1), "%")) %>%
  as.data.frame()
row.names(proportion) <- row.names(intersect)
intersect.spdf <- SpatialPolygonsDataFrame(intersect, proportion)

Toy model: solution (Colombia)

map %>%
  addPolygons(data = buffers, weight = 2, fillOpacity = 0) %>%
  addPolygons(
    data = intersect.spdf, fillColor = "red", fillOpacity = .9, weight = 2,
    label = ~ scales::percent(proportion),
    labelOptions = labelOptions(opacity = .75, direction = "auto")
  )

What is Shiny?

Shiny

library(datasets)

# Define a server for the Shiny app
function(input, output) {

  # Fill in the spot we created for a plot
  output$phonePlot <- renderPlot({

    # Render a barplot
    barplot(
      WorldPhones[, input$region] * 1000,
      main = input$region,
      ylab = "Number of Telephones",
      xlab = "Year"
    )
  })
}

# Use a fluid Bootstrap layout
fluidPage(    
  # Give the page a title
  titlePanel("Telephones by region"),
  # Generate a row with a sidebar
  sidebarLayout(      
    # Define the sidebar with one input
    sidebarPanel(
      selectInput("region", "Region:", choices=colnames(WorldPhones)),
      hr(),
      helpText("Data from AT&T (1961) The World's Telephones.")
    ),
    # Create a spot for the barplot
    mainPanel(
      plotOutput("phonePlot")  
    ))
  )

From Shiny Gallery

From Shiny Gallery

Shout

Infrastructure

Shout

Master Sampling Projetc

Goiana

Shout

Questions?